This paper presents a novel image processing algorithm utilizing quantum computing principles, specifically focusing on adaptive transformations. Traditional image processing algorithms often rely on explicit, computationally intensive procedures that struggle to effectively handle dynamic image variations. We propose a quantum-enhanced approach that leverages quantum entanglement and superposition to achieve a degree of self-adaptation, allowing for more robust and efficient image manipulation. This work explores the potential of quantum computing to significantly improve the performance of image processing tasks, particularly in scenarios involving complex and rapidly changing visual data. The core mechanism revolves around implementing a quantum-enhanced geometric transformation process, incorporating quantum error correction to maintain the integrity of the transformation. The algorithm will be discussed in detail, including the theoretical underpinnings, implementation considerations, and preliminary results demonstrating the potential of this approach.This paper presents a novel image processing algorithm utilizing quantum computing principles, specifically focusing on adaptive transformations. Traditional image processing algorithms often rely on explicit, computationally intensive procedures that struggle to effectively handle dynamic image variations. We propose a quantum-enhanced approach that leverages quantum entanglement and superposition to achieve a degree of self-adaptation, allowing for more robust and efficient image manipulation. This work explores the potential of quantum computing to significantly improve the performance of image processing tasks, particularly in scenarios involving complex and rapidly changing visual data. The core mechanism revolves around implementing a quantum-enhanced geometric transformation process, incorporating quantum error correction to maintain the integrity of the transformation. The algorithm will be discussed in detail, including the theoretical underpinnings, implementation considerations, and preliminary results demonstrating the potential of this approach.

Sep 6, 20260 citations

Quantum Image Processing and Its Applications

J. Jennifer Ranjani

Springer eBooksJan 1, 20190 citations

Quantum image scrambling algorithm based on discrete Baker map

Chengan Hou, Xingbin Liu, Songyang Feng

Quantum image processing has become a significant aspect within the field of quantum information processing because the image is an essential carrier of information, and quantum computation has powerful image processing ability. Image scrambling algorithms are often required as initial image operations in quantum image processing applications such as quantum image encryption and watermarking. However, the efficiency of existing quantum image scrambling algorithms needs to be improved urgently, especially in terms of periodicity. Therefore, a novel quantum image scrambling algorithm based on discrete Baker map is proposed in this paper, which can be implemented by swapping qubits with low circuit complexity. The quantum version of discrete Baker map is deduced and the corresponding quantum circuit is designed. The simulation results show that the scrambling algorithm has the characteristic of long period, which can further enhance the security of quantum image encryption algorithms. Besides, for generalized discrete Baker maps, the conditions that they can be implemented by swapping qubits are given. Moreover, the number of discrete Baker maps satisfying the conditions is also calculated.Quantum image processing has become a significant aspect within the field of quantum information processing because the image is an essential carrier of information, and quantum computation has powerful image processing ability. Image scrambling algorithms are often required as initial image operations in quantum image processing applications such as quantum image encryption and watermarking. However, the efficiency of existing quantum image scrambling algorithms needs to be improved urgently, especially in terms of periodicity. Therefore, a novel quantum image scrambling algorithm based on discrete Baker map is proposed in this paper, which can be implemented by swapping qubits with low circuit complexity. The quantum version of discrete Baker map is deduced and the corresponding quantum circuit is designed. The simulation results show that the scrambling algorithm has the characteristic of long period, which can further enhance the security of quantum image encryption algorithms. Besides, for generalized discrete Baker maps, the conditions that they can be implemented by swapping qubits are given. Moreover, the number of discrete Baker maps satisfying the conditions is also calculated.

Modern Physics Letters AApr 22, 202030 citations

Quantum Computing in Image Processing

Sanjay Chakraborty, Lopamudra Dey

Data-intensive researchJan 1, 20231 citation

Exploring quantum edge detection for sheep body contours in images

Santiago Merino-Bajo, Lídia Sánchez-González, Vicente Matellán Olivera

Abstract Quantum computing is being explored to tackle complex problems, such as image processing in this specific case. The ability of qubits to exist in multiple states at once and take advantage of quantum phenomena such as superposition and entanglement could have significant applications in a variety of fields, including image processing. Understandably, at this early stage in the development of quantum computing, quantum algorithms are not yet as efficient as classical algorithms for certain tasks. However, as quantum technology advances and becomes more accessible, we are likely to see significant improvements in terms of efficiency and problem-solving capabilities. The use of quantum computing to process images and detect specific features, such as the body contour of sheep for weight assessment or disease detection, has the potential to be a valuable tool in the agriculture and livestock industry. In this paper, we present the results of a quantum edge detector by analysing how the resolution affects the image result. Thus, a quantum edge detection model is implemented in the quantum simulator to analyse the edge recovery capability of an image as a function of the number of pixels in order to test the efficiency of quantum computers for image processing. The experiment results illustrate that edge detection times using quantum algorithms exceed 9000 seconds on 128x128 pixel images. Although the results are worse than those obtained using classical image processing models, they are promising since a resolution of 64$\times $64 or 128$ \times $128 is precise enough to determine the body contour of the sheep and can be executed in a quantum simulator. With the evolution of quantum computers, we are approaching the possibility of being able to use them for image processing tasks.Abstract Quantum computing is being explored to tackle complex problems, such as image processing in this specific case. The ability of qubits to exist in multiple states at once and take advantage of quantum phenomena such as superposition and entanglement could have significant applications in a variety of fields, including image processing. Understandably, at this early stage in the development of quantum computing, quantum algorithms are not yet as efficient as classical algorithms for certain tasks. However, as quantum technology advances and becomes more accessible, we are likely to see significant improvements in terms of efficiency and problem-solving capabilities. The use of quantum computing to process images and detect specific features, such as the body contour of sheep for weight assessment or disease detection, has the potential to be a valuable tool in the agriculture and livestock industry. In this paper, we present the results of a quantum edge detector by analysing how the resolution affects the image result. Thus, a quantum edge detection model is implemented in the quantum simulator to analyse the edge recovery capability of an image as a function of the number of pixels in order to test the efficiency of quantum computers for image processing. The experiment results illustrate that edge detection times using quantum algorithms exceed 9000 seconds on 128x128 pixel images. Although the results are worse than those obtained using classical image processing models, they are promising since a resolution of 64$\times $64 or 128$\times $128 is precise enough to determine the body contour of the sheep and can be executed in a quantum simulator. With the evolution of quantum computers, we are approaching the possibility of being able to use them for image processing tasks.

Logic Journal of IGPLMay 3, 20250 citations

Efficient representations of digital images on quantum computers

Norhan Nasr, Ahmed Younes, Ashraf Elsayed

Multimedia Tools and ApplicationsAug 31, 202131 citations

64 or 128

\times $128 is precise enough to determine the body contour of the sheep and can be executed in a quantum simulator. With the evolution of quantum computers, we are approaching the possibility of being able to use them for image processing tasks.